Lead, Credit Decision Science and AI

Wisr

. Lead the analytics and AI capability within Credit Risk, Data & Analytics .

Posted 9/21/2026full-timeSydney • AustraliaSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading analytics and AI capabilities within credit risk, focusing on model development, deployment, and governance in a regulated environment. Proficient in Python, SQL, and tools like Snowflake and Dataiku to drive automation and improve technical maturity across credit decisioning processes.

Highest-signal resume keywords

PythonSQLCredit Risk ModellingAI Solutions DeploymentAgile Delivery

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Hard Skills

Credit DecisioningBehavioural ModelsECL ModelsModel Lifecycle ManagementData Pipeline DevelopmentModel MonitoringAutomated ProcessesTest-and-Learn InitiativesVersion ControlReproducibility

Soft Skills

Confident CommunicationTeam LeadershipMentoringCollaborationStakeholder Engagement

Tools & Technologies

SnowflakeDataikuPythonSQLJiraPower BITableau

Certifications & Qualifications

IFRS 9 ECL MethodologyAustralian Consumer Credit Regulation

Industry Keywords

NCCPRG 209AFCAPrivacyCredit ReportingAgentic AI FrameworksLLM APIs

Tech Stack

Tools & technologies

PythonSQLTableau

About the role

Key responsibilities & impact

Lead the analytics and AI capability within Credit Risk, Data & Analytics
Oversee modelling work across credit decisioning, risk-based pricing, portfolio monitoring and IFRS 9 ECL
Own the analytics and modelling stack across Snowflake, Dataiku and Python
Improve technical maturity across version control, reproducibility, model monitoring and deployment discipline
Move recurring analysis into automated, production-supported processes
Lead adoption of AI and GenAI tooling across credit risk workflows
Produce data products to evaluate AI-agent accuracy against actual outcomes
Architect AI-agent self-improvement loops and own feedback mechanisms for updated agent logic and retrained behaviour
Build and own data pipelines ingesting agent audit trails, reasoning and decisions for accuracy monitoring and benchmarking
Identify and prioritise AI use cases, build investment cases and measure benefits
Ensure AI solutions meet model governance, validation and monitoring standards in a regulated lending environment
Lead development, validation, deployment and monitoring of credit decisioning, behavioural and ECL models, plus pricing and offer logic
Design and run test-and-learn initiatives for credit policy and pricing changes
Work with Equifax, Illion, bank statement and open banking data
Run team delivery on the CRDA Jira board, including story shaping, backlog grooming and sprint prioritisation
Collaborate with Data Engineering, Product and Technology on platform dependencies and roadmap sequencing
Translate model outputs into decisions for Credit, Pricing, Collections, Product and Finance
Prepare and present analytical papers to Credit Committee, Risk Committee and board audiences
Maintain modelling, validation, governance and monitoring practices aligned with NCCP, RG 209 and ASIC responsible lending obligations
Lead, mentor and develop analysts and data scientists
Manage team priorities, delivery and quality standards

Requirements

What you’ll need

Strong Python and SQL
Understanding of the full model lifecycle: development, deployment, monitoring and maintenance
Strong background in credit risk modelling within a lending environment, including credit decisioning, behavioural, pricing or ECL models
Hands-on experience delivering AI or GenAI solutions into production business processes
Proven experience leading or mentoring an analytics or data science team
Experience running delivery in an Agile environment using Jira, including backlog grooming, sprint planning, story sizing and prioritisation
Confident communication with non-technical stakeholders and senior leadership
Dataiku, Snowflake, Power BI or Tableau advantageous
IFRS 9 ECL methodology and provisioning advantageous
Australian consumer credit regulation, including NCCP, RG 209, AFCA, privacy and credit reporting, advantageous
Agentic AI frameworks and LLM APIs applied to document or data-heavy workflows advantageous
ML deployment advantageous

Benefits

Comp & perks

Flexible and hybrid working
$500 every year to spend on your wellbeing
An extra Annual Leave day off every financial year through A Day on Wisr
Unlimited access to LinkedIn Learning
Access to ClassPass for fitness and wellness options
Generous paid parental leave
Regular social events and team offsites
Employee Assistance Program, Uprise, with up to 6 coaching sessions per year
Psychological wellbeing and safety support
Reasonable adjustments to the interview process